ML Anthology

Paninski, Liam

52 publications

ICLR 2025 In Vivo Cell-Type and Brain Region Classification via Multimodal Contrastive Learning Han Yu, Hanrui Lyu, YiXun Xu, Charlie Windolf, Eric Kenji Lee, Fan Yang, Andrew M Shelton, Olivier Winter, International Brain Laboratory, Eva L Dyer, Chandramouli Chandrasekaran, Nicholas A. Steinmetz, Liam Paninski, Cole Lincoln Hurwitz
NeurIPS 2025 Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets Ji Xia, Yizi Zhang, Shuqi Wang, Genevera I. Allen, Liam Paninski, Cole Lincoln Hurwitz, Kenneth D. Miller
ICML 2025 Neural Encoding and Decoding at Scale Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre, Zixuan Wang, Hanrui Lyu, International Brain Laboratory, Eva L Dyer, Liam Paninski, Cole Lincoln Hurwitz
ICMLW 2024 Amortized Probabilistic Detection of Communities in Graphs Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski, Ari Pakman
NeurIPS 2024 Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution Yizi Zhang, Yanchen Wang, Donato M. Jiménez-Benetó, Zixuan Wang, Mehdi Azabou, Blake Richards, Renee Tung, Olivier Winter, The International Brain Laboratory, Eva Dyer, Liam Paninski, Cole Hurwitz
NeurIPS 2023 Bayesian Target Optimisation for High-Precision Holographic Optogenetics Marcus Triplett, Marta Gajowa, Hillel Adesnik, Liam Paninski
NeurIPS 2023 Bypassing Spike Sorting: Density-Based Decoding Using Spike Localization from Dense Multielectrode Probes Yizi Zhang, Tianxiao He, Julien Boussard, Charles Windolf, Olivier Winter, Eric Trautmann, Noam Roth, Hailey Barrell, Mark Churchland, Nicholas A Steinmetz, Erdem Varol, Cole Hurwitz, Liam Paninski
NeurIPS 2023 Towards Robust and Generalizable Representations of Extracellular Data Using Contrastive Learning Ankit Vishnubhotla, Charlotte Loh, Akash Srivastava, Liam Paninski, Cole Hurwitz
JMLR 2021 A General Linear-Time Inference Method for Gaussian Processes on One Dimension Jackson Loper, David Blei, John P. Cunningham, Liam Paninski
NeurIPS 2021 Three-Dimensional Spike Localization and Improved Motion Correction for Neuropixels Recordings Julien Boussard, Erdem Varol, Hyun Dong Lee, Nishchal Dethe, Liam Paninski
NeurIPS 2020 Deep Graph Pose: A Semi-Supervised Deep Graphical Model for Improved Animal Pose Tracking Anqi Wu, Estefany Kelly Buchanan, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora Angelaki, Andrés Bendesky, The International Brain Laboratory The International Brain Laboratory, John P. Cunningham, Liam Paninski
ECML-PKDD 2020 Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model Ding Zhou, Yuanjun Gao, Liam Paninski
ICML 2020 Neural Clustering Processes Ari Pakman, Yueqi Wang, Catalin Mitelut, Jinhyung Lee, Liam Paninski
NeurIPS 2020 Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations Joshua Glaser, Matthew Whiteway, John P. Cunningham, Liam Paninski, Scott Linderman
NeurIPS 2019 BehaveNet: Nonlinear Embedding and Bayesian Neural Decoding of Behavioral Videos Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R Datta, Scott Linderman, Liam Paninski
NeurIPS 2019 Efficient Characterization of Electrically Evoked Responses for Neural Interfaces Nishal Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan Litke, Liam Paninski, E. J. Chichilnisky
NeurIPS 2019 Scalable Bayesian Inference of Dendritic Voltage via Spatiotemporal Recurrent State Space Models Ruoxi Sun, Scott Linderman, Ian Kinsella, Liam Paninski
NeurIPSW 2019 Spike Sorting Using the Neural Clustering Process Yueqi Wang, Ari Pakman, Catalin Mitelut, JinHyung Lee, Liam Paninski
AISTATS 2018 Reparameterizing the Birkhoff Polytope for Variational Permutation Inference Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham
ICML 2018 Scalable Approximate Bayesian Inference for Particle Tracking Data Ruoxi Sun, Liam Paninski
AISTATS 2017 Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski
ICLR 2017 Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses Eleanor Batty, Josh Merel, Nora Brackbill, Alexander Heitman, Alexander Sher, Alan M. Litke, E. J. Chichilnisky, Liam Paninski
NeurIPS 2017 Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal, E. J. Chichilnisky, Liam Paninski
NeurIPS 2017 OnACID: Online Analysis of Calcium Imaging Data in Real Time Andrea Giovannucci, Johannes Friedrich, Matt Kaufman, Anne Churchland, Dmitri Chklovskii, Liam Paninski, Eftychios A Pnevmatikakis
AISTATS 2017 Scalable Variational Inference for Super Resolution Microscopy Ruoxi Sun, Evan Archer, Liam Paninski
ICML 2017 Stochastic Bouncy Particle Sampler Ari Pakman, Dar Gilboa, David Carlson, Liam Paninski
NeurIPS 2017 YASS: Yet Another Spike Sorter Jin Hyung Lee, David E Carlson, Hooshmand Shokri Razaghi, Weichi Yao, Georges A Goetz, Espen Hagen, Eleanor Batty, E. J. Chichilnisky, Gaute T. Einevoll, Liam Paninski
NeurIPS 2016 Automated Scalable Segmentation of Neurons from Multispectral Images Uygar Sümbül, Douglas Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward Boyden, Liam Paninski
NeurIPS 2016 Fast Active Set Methods for Online Spike Inference from Calcium Imaging Johannes Friedrich, Liam Paninski
NeurIPS 2016 Linear Dynamical Neural Population Models Through Nonlinear Embeddings Yuanjun Gao, Evan W Archer, Liam Paninski, John P. Cunningham
ICML 2016 Partition Functions from Rao-Blackwellized Tempered Sampling David Carlson, Patrick Stinson, Ari Pakman, Liam Paninski
NeurIPS 2014 Clustered Factor Analysis of Multineuronal Spike Data Lars Buesing, Timothy A Machado, John P. Cunningham, Liam Paninski
NeurIPS 2013 A Multi-Agent Control Framework for Co-Adaptation in Brain-Computer Interfaces Josh S Merel, Roy Fox, Tony Jebara, Liam Paninski
NeurIPS 2013 Auxiliary-Variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions Ari Pakman, Liam Paninski
NeurIPS 2013 Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits Ben Shababo, Brooks Paige, Ari Pakman, Liam Paninski
NeurIPS 2013 Robust Learning of Low-Dimensional Dynamics from Large Neural Ensembles David Pfau, Eftychios A Pnevmatikakis, Liam Paninski
NeurIPS 2013 Sparse Nonnegative Deconvolution for Compressive Calcium Imaging: Algorithms and Phase Transitions Eftychios A Pnevmatikakis, Liam Paninski
AISTATS 2012 Fast Interior-Point Inference in High-Dimensional Sparse, Penalized State-Space Models Eftychios Pnevmatikakis, Liam Paninski
AISTATS 2012 Low Rank Continuous-Space Graphical Models Carl Smith, Frank Wood, Liam Paninski
NeurIPS 2011 Information Rates and Optimal Decoding in Large Neural Populations Kamiar R. Rad, Liam Paninski
NeurIPS 2008 Designing Neurophysiology Experiments to Optimally Constrain Receptive Field Models Along Parametric Submanifolds Jeremy Lewi, Robert Butera, David M. Schneider, Sarah Woolley, Liam Paninski
AISTATS 2007 Efficient Active Learning with Generalized Linear Models Jeremy Lewi, Robert Butera, Liam Paninski
NeurIPS 2006 Real-Time Adaptive Information-Theoretic Optimization of Neurophysiology Experiments Jeremy Lewi, Robert Butera, Liam Paninski
NeurIPS 2005 Large-Scale Biophysical Parameter Estimation in Single Neurons via Constrained Linear Regression Misha Ahrens, Liam Paninski, Quentin J. Huys
NeurIPS 2005 Nonparametric Inference of Prior Probabilities from Bayes-Optimal Behavior Liam Paninski
NeurIPS 2004 Log-Concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning Liam Paninski
NeCo 2004 Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli
NeurIPS 2004 Variational Minimax Estimation of Discrete Distributions Under KL Loss Liam Paninski
NeurIPS 2003 Design of Experiments via Information Theory Liam Paninski
NeCo 2003 Estimation of Entropy and Mutual Information Liam Paninski
NeurIPS 2003 Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model Liam Paninski, Eero P. Simoncelli, Jonathan W. Pillow
NeurIPS 2002 Convergence Properties of Some Spike-Triggered Analysis Techniques Liam Paninski